Learning low-dimensional embeddings of the nodes in a graph is a fundamental technique underlying state-of-the-art approaches to link prediction and recommender systems [1]}.
However, in many applications—especially those involving social graphs—it is desirable to exercise control over the information contained within learned node embeddings.
For instance, we may want to ensure that recommendations are fair or balanced with respect to certain attributes (e.g., that they do not depend on a user's race or gender) or we may want to ensure privacy by not exposing certain attributes through learned node representations.
In this work we investigate the feasibility of enforcing such invariance constraints on (social) graph embeddings.
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